Automated lumen definition from 30 MHz intravascular ultrasound images

One prerequisite for standard clinical use of intravascular ultrasound imaging is rapid evaluation of the data. The main quantities to be extracted from the data are the size and the shape of the lumen. Until now, no accurate, robust and reproducible method to obtain the lumen boundaries from intravascular ultrasound images has been described. In this study, 21 different (semi-)automated binary-segmentation methods for determining the lumen are compared with manual segmentation to find an alternative for the laborious and subjective procedure of manual editing. After a preprocessing step in which the catheter area is filled with lumen-like grey values, all approaches consist of two steps: (i) smoothing the images with different filtering methods and (ii) extracting the lumen by an object definition method. The combination of different filtering methods and object definition methods results in a total of 21 methods and 80 experiments. The results are compared with a reference image, obtained from manual editing, by use of four different quality parameters--two based on squared distances and two based on Mahalanobis distances. The evaluation has been carried out on 15 images, of which seven are obtained before balloon dilation and eight after balloon dilation. While for the post-dilation images no definite conclusions can be drawn, an automated contour model applied to images smoothed with a large kernel appears to be a good alternative to manual contouring. For pre-dilation images a fully automated active contour model, initialized by thresholding, preceded by filtering with a small-scale median filter is the best alternative for manual delineation. The results of this method are even better than manual segmentation, i.e. they are consistently closer to the reference image than the average distance of all individual manual segmentations.     

图像分割评估方法在显微图像分析中的应用

图像分割是计算机视觉领域中的重要分支,旨在将图像分成若干个特定的、具有独特性质的区域。随着计算机硬件计算能力的提高和计算方法的进步,大量基于不同理论的图像分割算法获得了长足的发展。因而选择合适的评估方法对分割结果的准确性和适用性进行综合评估,从而选择最优分割算法,成为图像分割研究中的必要环节。在综述14种图像分割评估指标的基础上,将其分成基于像素的评估方法、基于类内重合度的评估方法、基于边界的评估方法、基于聚类的评估方法和基于实例的评估方法五大类。在材料显微图像分析的应用背景下,通过实验讨论了不同分割方法和不同典型噪声在不同评估方法中的表现。最终,讨论了各种评估方法的优势和适用性。      

Watershed-based segmentation of 3D MR data for volume quantization

The aim of this work is the development of a semiautomatic segmentation technique for efficient and accurate volume quantization of Magnetic Resonance (MR) data. The proposed technique uses a 3D variant of Vincent and Soilles immersion-based watershed algorithm that is applied to the gradient magnitude of the MR data and that produces small volume primitives. The known drawback of the watershed algorithm, oversegmentation, is strongly reduced by a priori application of a 3D adaptive anisotropic diffusion filter to the MR data. Furthermore, oversegmentation is a posteriori reduced by properly merging small volume primitives that have similar gray level distributions. The outcome of the proceeding image processing steps is presented to the user for manual segmentation. Through selection of volume primitives, the user quickly segments of first slice, which contains the object of interest. Afterwards, the subsequent slices are automatically segmented by extrapolation. Segmentation results are contingently manually corrected. The proposed segmentation technique is tested on phantom objects, where segmentation errors less than 2% are observed. In addition, the technique is demonstrated on 3D MR data of the mouse head from which the cerebellum is extracted. Volumes of the mouse cerebellum and the mouse brains in toto are calculated.     

Split-and-merge segmentation of magnetic resonance medical images: performance evaluation and extension to three dimensions

Intensity- or edge-based methods of segmentation are often insufficiently robust to be applied to images containing complex anatomical objects, such as those seen in high-resolution magnetic resonance imaging systems. Split-and-merge techniques attempt to overcome these difficulties by using homogeneity measures. Simple modifications to the basic 2D split-and-merge method, based on the principles of simulated annealing and controlled boundary elimination, are developed and discussed. Simulated annealing reduced the number of regions by 22% with a further reduction of 21% achieved through boundary elimination. Smoother regional boundaries are also produced. These methods are extended to true 3D and quantitatively compared with their 2D counterparts. The main advantage of 3D methods is that they produce segmented volumes by directly preserving the connectivity between slices, whereas in 2D, segments have to be grouped together in a post-split-and-merge process. Finally, the properties of the 3D approach are demonstrated by the automatic quantitation of brain ventricle volume, producing estimates to within 7% of validated manual methods.     

Blepharoplasty: A classification of selected techniques in the treatment and prevention of lower lid margin distortions

The topographic relations of complex structures and the morphogenesis of organ systems can only be fully understood in their three-dimensional context. Three-dimensional (3-D) reconstruction of physically sectioned specimens has become an indispensable tool in modern anatomical and embryological research. Teaching also makes increasingly use of 3-D representations, in particular in the case of embryonic systems that undergo complicated transformations of form and shape. At present no cheap and simple technique is available that generates accurate 3-D models of sectioned objects. In this study we describe a novel technique that rapidly provides faithful 3-D models of sectioned specimens. The images are captured directly from the cutting surface of the embedding block after each sectioning and "on block" staining step. Automatic image processing generates a stack of binary images of the specimen contour. Binary images of internal structures are obtained both by automatic segmentation and manual tracing. Since these image series are inherently aligned, they can be reconstructed three-dimensionally without time-consuming alignment procedures. The quality and the flexibility of the method are demonstrated by reconstructing three kinds of specimens of different histological composition and staining contrast: a 4 mm mouse embryo together with several of its inner organs, a cavernous sinus region of a human infant, and a segment of a human carotid artery. Very short processing times and the faithful representation of complex structural arrangements recommend this technique for routine use in morphological research and for creating embryologic teaching models or 3-D embryonic staging series.     

Method for segmenting chest CT image data using an anatomical model: preliminary results

We present an automated, knowledge-based method for segmenting chest computed tomography (CT) datasets. Anatomical knowledge including expected volume, shape, relative position, and X-ray attenuation of organs provides feature constraints that guide the segmentation process. Knowledge is represented at a high level using an explicit anatomical model. The model is stored in a frame-based semantic network and anatomical variability is incorporated using fuzzy sets. A blackboard architecture permits the data representation and processing algorithms in the model domain to be independent of those in the image domain. Knowledge-constrained segmentation routines extract contiguous three-dimensional (3-D) sets of voxels, and their feature-space representations are posted on the blackboard. An inference engine uses fuzzy logic to match image to model objects based on the feature constraints. Strict separation of model and image domains allows for systematic extension of the knowledge base. In preliminary experiments, the method has been applied to a small number of thoracic CT datasets. Based on subjective visual assessment by experienced thoracic radiologists, basic anatomic structures such as the lungs, central tracheobronchial tree, chest wall, and mediastinum were successfully segmented. To demonstrate the extensibility of the system, knowledge was added to represent the more complex anatomy of lung lesions in contact with vessels or the chest wall. Visual inspection of these segmented lesions was also favorable. These preliminary results suggest that use of expert knowledge provides an increased level of automation compared with low-level segmentation techniques. Moreover, the knowledge-based approach may better discriminate between structures of similar attenuation and anatomic contiguity. Further validation is required.     

A multiresolution approach for contour extraction from brain images

Many image registration methods use head surface, brain surface, or inner/outer surface of the skull to estimate rotation and translation parameters. The inner surface of the skull is also used for intracranial volume segmentation which is considered the first step in segmentation and analysis of brain images. The surface is usually characterized by a set of edge or contour points extracted from cross-sectional images. Automatic extraction of contour points is complicated by discontinuity of edges in the back of the eyes and ears and sometimes by a previous surgery or an inadequate field of view. We have developed an automated method for contour extraction that connects discontinuities using a multiresolution pyramid. Steps of the method are: (1) Contour points are found by an edge-tracking algorithm; (2) A multiresolution pyramid of contour points is constructed; (3) Contour points of reduced images are found; (4) From the continuous contour found at the lowest resolution, contour points at a higher resolution are found; (5) Step 4 is repeated until contour points at the highest resolution (original image) are found. The method runs fast and has been successful in extracting contours from MRI and CT images. We illustrate the method and its performance using MRI and CT images of the human brain.     

A large screen digitizer system for radiation therapy treatment planning

PURPOSE: This paper describes a new technique for manually drawing contours of anatomy over image data for the purposes of radiation therapy treatment planning. METHODS AND MATERIALS: A large area rear-projectible digitizer tablet is used together with a projection TV system to display computer graphics and image data. Large images of computed tomography or magnetic resonance cross-sections are displayed and the digitizer is used to directly trace outlines of important organs. Digitizer menus allow multiple functions for selecting images and structures, for changing the grayscale level and window, and for zooming and roaming the image. RESULTS: This device has been in clinical operation for many years and has proven to greatly increase the speed of entering cross-sectional outlines defined for serial computed tomography images sets. A small timing study of clinical usage demonstrates up to a factor of ten improvement in the speed of contour entry. CONCLUSION: For 3-dimensional radiation therapy, tumor, and target volumes, as well as important critical organs, must be delineated from serial sets of computed tomography or magnetic resonance images. Often 30 or more slices must be considered and the process of outlining structures on this number of slices can represent a significant fraction of the total treatment planning time. The device described in this paper greatly improve the ease and speed of manual contour entry for 3-dimensional radiation therapy planning.     

Computer-assisted diagnosis based on computer-based image interpretation and 3D-visualization

PURPOSE: To survey methods for 3D data visualization and image analysis which can be used for computer based diagnostics. MATERIAL AND METHODS: The methods available are explained in short terms and links to the literature are presented. Methods which allow basic manipulation of 3D data are windowing, rotation and clipping. More complex methods for visualization of 3D data are multiplanar reformation, volume projections (MIP, semi-transparent projections) and surface projections. Methods for image analysis comprise local data transformation (e.g. filtering) and definition and application of complex models (e.g. deformable models). RESULTS: Volume projections produce an impression of the 3D data set without reducing the data amount. This supports the interpretation of the 3D data set and saves time in comparison to any investigation which requires examination of all slice images. More advanced techniques for visualization, e.g. surface projections and hybrid rendering visualize anatomical information to a very detailed extent, but both techniques require the segmentation of the structures of interest. Image analysis methods can be used to extract these structures (e.g. an organ) from the image data. DISCUSSION: At the present time volume projections are robust and fast enough to be used routinely. Surface projections can be used to visualize complex and presegmented anatomical features.     

基于FCM-WA联合算法的多种类矿石图像分割

矿石图像分割是基于机器视觉的矿石粒度分布检测的重要组成部分。针对复合矿山中颜色多样、纹理复杂且边缘粘连的多种类矿石图像难以识别与分割的问题,提出了一种基于FCM-WA联合算法的矿石图像分割方法。首先对矿石图像进行形态学优化,利用双边滤波、直方图均衡化和形态学重构来优化矿石图像的几何特征,减少噪声对分割效果的影响,提高图像对比度;然后将模糊C均值聚类（FCM）算法与分水岭（WA）算法相结合,利用FCM算法进行聚类迭代,计算出合适的分割阈值并对矿石图像进行分割,输出二值化图像;再利用基于距离变换的WA算法优化FCM算法的分割结果,对FCM算法输出的矿石图像边缘粘连部分进行分割,以获取最佳的分割图像。研究结果表明：（1）利用形态学优化流程处理矿石图像能够减少噪声并增强边缘信息,从而提高对比度;（2）相比传统的大津法和遗传算法,本文所提FCM-WA方法的稳健性更强、分割效果更好,对多种类的矿石图像像素分割准确率和矿石粒度识别准确率均可达到92%以上;（3）通过试验验证,FCM-WA方法能够精确地分割颜色多样、纹理特征复杂及边缘粘连的多种类矿石图像,分割结果满足粒度分布检测的要求;（4）FCM-...     

In vivo validation of tissue segmentation based on a 3D feature map using both a hamster brain tumor model and stereotactically guided biopsy of brain tumors in man

The purpose of this study was to validate our MR tissue segmentation technique using a hamster brain tumor model and malignant brain tumors in man. We used a multispectral tissue segmentation analysis. Three sets of MRI data were included: proton density, T2-weighted fast spin echo, and T1-weighted spin echo, as inputs. Three image preprocessing steps included correcting image nonuniformity, application of an anisotropic diffusion type filter, and data point selection by a qualified observer. We used the k-Nearest Neighbor segmentation algorithm, which does not require prior knowledge of the sample distribution. This choice allowed us to optimize the different tissue clusters present in three-dimensional (3D) feature space. In vivo validation of the technique was performed in hamsters harboring tumors induced with JC virus-transformed HJC-15 cells, as compared to three control animals. Human brain tumors obtained by stereotactically guided biopsy in six patients were also included in the study. Finally, brain tumors were removed from two patients who underwent conventional craniotomy using segmentation-derived images as a guide. In the hamsters, 10 tissues were correctly identified by segmentation and were confirmed histologically (P < .02). In the patients, there was also a strong correlation between our segmentation results and the tissue obtained by stereotactic biopsy (P < .01). In one of the two patients who underwent open craniotomy, segmentation images were useful in revealing tumor spread into vital areas of the brain (motor area). In conclusion, the results of segmentation correlate well with the tissues in vivo and thus warrant further clinical utilization and evaluation.     

基于免疫遗传形态学的视网膜光学相干断层图像边缘

提出了基于免疫遗传算法的形态学自适应结构元素生成算法,并将其用于光学相干断层成像(optical coherence tomography,OCT) 图像中视网膜组织边缘检测. 首先将图像进行去噪和粗分割的预处理,并将图像划分为若干子图像; 其次对每一子图利用免疫遗传算法求取自适应结构元,初始随机生成固定长度的二进制数串作为抗体,并将其转化为结构元素格式,以图像二维熵定义抗体适应度,根据子图像本身结构特征信息,寻找最优抗体结构元素; 最后利用寻优得到的各结构元素对子图进行形态学边缘检测,合并各子图的分割结果,实现整体图像目标边界提取. 实验结果表明了该方法在图像目标边界提取的有效性.      

基于DEM的露天矿坑坡面顶底线自动提取方法研究

针对露天矿坑坡面顶底线传统提取方法存在的低效率问题,提出了一种从露天矿坑DEM(数字高程模型,Digital Elevation Model)三维模型中自动提取坡顶底线的方法.首先对原始三维模型数据进行双边滤波预处理,进行去噪并保留特征点;其次根据高程值的标准差设定阈值后将DEM三维模型转化为灰度图像;最后采用梯度运算...     

Expression of integrin receptors on plasma membranes of primary corneal epithelial cells is matrix specific

The performance evaluation of a semi-supervised fuzzy c-means (SFCM) clustering method for monitoring brain tumor volume changes during the course of routine clinical radiation-therapeutic and chemo-therapeutic regimens is presented. The tumor volume determined using the SFCM method was compared with the volume estimates obtained using three other methods: (a) a k nearest neighbor (kNN) classifier, b) a grey level thresholding and seed growing (ISG-SG) method and c) a manual pixel labeling (GT) method for ground truth estimation. The SFCM and kNN methods are applied to the multispectral, contrast enhanced T1, proton density, and T2 weighted, magnetic resonance images (MRI) whereas the ISG-SG and GT methods are applied only to the contrast enhanced T1 weighted image. Estimations of tumor volume were made on eight patient cases with follow-up MRI scans performed over a 32 week interval during treatment. The tumor cases studied include one meningioma, two brain metastases and five gliomas. Comparisons with manually labeled ground truth estimations showed that there is a limited agreement between the segmentation methods for absolute tumor volume measurements when using images of patients after treatment. The average intraobserver reproducibility for the SFCM, kNN and ISG-SG methods was found to be 5.8%, 6.6% and 8.9%, respectively. The average of the interobserver reproducibility of these methods was found to be 5.5%, 6.5% and 11.4%, respectively. For the measurement of relative change of tumor volume as required for the response assessment, the multi-spectral methods kNN and SFCM are therefore preferred over the seedgrowing method.     

Basic principles and historical consideration of MR cholangiopancreatography

Basic principle of MR cholangiopancreatography (MRCP) is heavily T2-weighted imaging (hydrography) to use bile and pancreatic juice as "natural contrast medium". Firstly developed sequence for MRCP was a CE-FAST sequence, which employed time-reversed FID signal. The current most popular sequences for MRCP are single-shot fast spin-echo sequences, which are divided into three types (2D single slice, 2D mutiple slice and 3D methods). The advantage of 2D single slice method is conveniently obtained projection imaging within a few seconds of examination time. Both 2D multiple slice and 3D methods consists of a MIP image and a series of source images. The MIP image creates global images of pancreatico-biliary system. The source images provide detailed evaluation of various anatomical structures and abnormalities. By using these sequences properly, MRCP can yield valuable informations of pancreatico-biliary diseases non-invasively.     

基于数字条纹投影的钢板表面三维缺陷检测

针对目前常用的钢板表面检测系统在检测三维缺陷时存在的局限性,提出了一种基于数字条纹投影的三维缺陷检测方法,并构建了检测系统的数学模型,首先通过面阵CCD相机采集由表面三维缺陷引起的投射条纹形变图像,然后使用旋滤波算法对条纹图像预处理,并通过傅里叶变换轮廓术算法实现对条纹相位的提取,最终利用数学模型推导出条纹相位与缺陷深度的关系,进行钢板表面三维缺陷的在线检测.试验结果表明,该方法在准确检出钢板表面三维缺陷(凹坑或凸包)的同时较好地解决了钢板在传送中的振动及二维缺陷干扰等问题.     

Evaluation of segmentation algorithms for generation of patient models in radiofrequency hyperthermia

Time-efficient and easy-to-use segmentation algorithms (contour generation) are a precondition for various applications in radiation oncology, especially for planning purposes in hyperthermia. We have developed the three following algorithms for contour generation and implemented them in an editor of the HyperPlan hyperthermia planning system. Firstly, a manual contour input with numerous correction and editing options. Secondly, a volume growing algorithm with adjustable threshold range and minimal region size. Thirdly, a watershed transformation in two and three dimensions. In addition, the region input function of the Helax commercial radiation therapy planning system was available for comparison. All four approaches were applied under routine conditions to two-dimensional computed tomographic slices of the superior thoracic aperture, mid-chest, upper abdomen, mid-abdomen, pelvis and thigh; they were also applied to a 3D CT sequence of 72 slices using the three-dimensional extension of the algorithms. Time to generate the contours and their quality with respect to a reference model were determined. Manual input for a complete patient model required approximately 5 to 6 h for 72 CT slices (4.5 min/slice). If slight irregularities at object boundaries are accepted, this time can be reduced to 3.5 min/slice using the volume growing algorithm. However, generating a tetrahedron mesh from such a contour sequence for hyperthermia planning (the basis for finite-element algorithms) requires a significant amount of postediting. With the watershed algorithm extended to three dimensions, processing time can be further reduced to 3 min/slice while achieving satisfactory contour quality. Therefore, this method is currently regarded as offering some potential for efficient automated model generation in hyperthermia. In summary, the 3D volume growing algorithm and watershed transformation are both suitable for segmentation of even low-contrast objects. However, they are not always superior to user-friendly manual programs for contour generation. When the volume growing algorithm is used, the contours have to be postprocessed with suitable filters. The watershed transformation has a large potential if appropriately developed to 3D sequences and 3D interaction features. After all, the practicality and feasibility of every segmentation method critically depend on various details of the user software as pointed out in this article.     

Systematic investigations of the contrast results of histochemical stainings of neurons and glial cells in the human brain by means of image analysis

The investigation of neurohistological specimens by image analysis has become an important tool in morphological neuroscience. The problems which arise during the processing of these images are non-trivial, especially if a pattern recognition of cells in the imaged tissue is intended. One of the major problems faced concerns the segmentation of structures of interest, whether cells or other histologic structures. The segmentation problem is often the result of an inappropriate staining procedure. For serious image analysis to be performed, the material under investigation must be optimally prepared. Spatially complex patterns, e.g. fuzzy-like neighbouring neurons, are easy to recognize for humans. But the integrative and associative performance of current artificial neuronal network schemes is too low to achieve the same recognition quality as humans do. Therefore, a general analysis of staining characteristics was performed, especially with respect to those stains which are relevant to object segmentation. Although most image analytical investigations of tissues are based on stained samples, a study of this type has not been previously conducted. Of the stains and procedures evaluated, the gallocyanin chrome alum combination staining provided the best stain contrast. Furthermore, this staining method shows sufficient constancy within different parts of the human brain. Even the fine nuclear textures are differentiable and can be used for further pattern recognition procedures.     

Automated detection of BB pixel clusters in digital fluoroscopic images

Small ball bearings (BBs) are often used to characterize and correct for geometric distortion of x-ray image intensifiers. For quantitative applications the number of BBs required for accurate distortion correction is prohibitively large for manual detection. A method to automatically determine the BB coordinates is described. The technique consists of image segmentation, pixel coalescing and centroid calculation. The dependence of calculated BB coordinates on segmentation threshold was also evaluated and found to be within the uncertainty of measurement.     

FASE (fast advanced spin echo)

RF-refocused single or multi-shot EPI provides high-contrast 2D or 3D T2-weighted images in a very short scan time. Applications include MR cholangiopancreatography, MR myelography, imaging detailed structures of the internal auditory canal and in situations in which very fast T2 imaging is required. FASE offers both 2D and 3D techniques. 2D FASE technique permits high-resolution images of 384 matrix or more to be obtained at 2 to 3 seconds per image. It is easy to perform and suitable for screening. 3D FASE permits acquisition of isotropic voxels, allowing high-resolution viewing from any desired direction by post-processing (MIP and/or MPR). This technique is best suited to detailed examinations in which multiple projection angles will be reconstructed or high-resolution diagnosis of source images.